According to our study, the correlation is moderate between EQ-5D scores and SF-36 scores. Using the IRT model, we found the EQ-5D scale score is moderately correlated with SF-36 PCS and SF-36 total score. Patients with stroke, psychiatric disorder, COPD, cancer, and osteoarthritis have a higher chance of impaired quality of life. The item information functions reveal that patients with chronic diseases and HRQoL below the 10th percentile could be better differentiated by the first three items of EQ-5D than the other items.
The EQ-5D scale has only 6 items, far fewer than 36 items of the SF-36 scale. For survey takers, it takes at least 10 minutes to complete the SF-36 scale, but it only takes less than 2 minutes to fill out the EQ-5D scale. The EQ-5D scale can bring potential benefits by saving time and money for the purpose of public health and clinical investigation. It is also of great importance that each country validates their own use of the EQ-5D scores, which will inform future practice in the local context. Using the IRT graded response model for quality of life research has been rarely seen in the previous literature in Taiwan, but it provides many insights into the analysis and interpretation of EQ-5D scores. Since our sample was representative of the national population in Taiwan, we can have an estimate of the average HRQoL using the EQ-5D scale score from the graded response model and establish norms for comparison in the future.
Our study shows that both the EQ-5D index (the average score of the first five items) and the EQ-5D scale score (the ability value estimated from the IRT model) have a moderate correlation with the SF-36 total scores. Although the EQ-5D index and the EQ-5D scale score share similar correlation coefficients with the external criterion, the EQ-5D scale score has more information, because the graded response model weights each item according to its information. The correlation coefficient between the EQ-5D index and the EQ-5D scale score is 0.70 (far from 0.99), supporting that the EQ-5D scale score is providing different information than the EQ-5D index does.
The information function from the IRT graded response model helps clarify which items are more informative at a specific range of the EQ-5D scale score. In our findings, three items, D1. Mobility, D2. Self-care, and D3. Usual activities, provide much more information for interviewees with an EQ-5D scale score near − 2 than the other items do. For patients who have chronic diseases and an EQ-5D scale score below the 10th percentile, the first three items of the EQ-5D scale are useful to tell whether their quality of life is impaired (very low or low). Clinicians can have these three items as a set of screening questions if they encounter a patient with chronic diseases and suspected impaired quality of life. If the patient reports any decreased function in these three items, the clinician should arrange the corresponding management plan to improve (or at least maintain) the patient’s health state and quality of life.
One thing worth mentioning is that we have dichotomized the first five items of the EQ-5D scale and divided the scores of sixth item into eleven parts (0, 1, 2, …, 10) for the IRT graded response model. This version of scale showed concentrated information around a scale score of -2. Keeping the first three items of the EQ-5D scale with only 2 score points can be an even more efficient way and provide us adequate information to differentiate patients who have very low and low levels of quality of life. We suggest to use the 2-point scale in the clinic setting for its relative convenience.
In this NHIS dataset, we registered a variety of chronic diseases that were diagnosed by the physicians rather than simply reported by the interviewees. When examining the EQ-5D scale scores by disease subgroups, we found patients with different types of chronic diseases had different levels of HRQoL to various degrees. Patients with stroke, psychiatric disorder, COPD, cancer, and osteoarthritis have a higher chance of impaired quality of life. Clinicians need to be attentive to these subgroups of patients with chronic diseases. The EQ-5D scale can be a useful tool to assess whether the quality of life is impaired among the high-risk patient population.
Although IRT shows great benefits by revealing the item characteristics, there are some considerations when using the IRT graded response model. First, for polytomous items like EQ-5D VAS, a large sample size (above 3,500) and coverage across polytomous item scales are needed . The case number in our sampling is large enough for us to fit the IRT model. Second, we can only gather information from the given items of our scale. If the goal is to find more details in each dimension of EQ-5D, an in-depth survey with more items is needed. According to our correlation analysis, the EQ-5D scale score itself is a moderate predictor for the SF-36 score. The finding supports that the EQ-5D scale could be a useful and efficient alternative of SF-36 to quickly screen patients’ HRQoL under time constraints. However, if we want to understand how patients with different diseases have different quality of life, it is vital to examine the EQ-5D scores for each type of chronic disease and link them to scores of other HRQoL measures with more items in the following studies.
The EQ-5D scores in our study demonstrate a higher correlation with the SF-36 physical component score than with the mental component score. Some diseases are known to be highly associated with mental health problems and may show a different pattern of information function of EQ-5D items in the IRT graded response model if the sample targets the population of people with these diseases. Similar issues have been raised in a previous review of psychometrics and qualitative assessment of EQ-5D . Therefore, future research using IRT is needed to understand how to interpret the scores of EQ-5D items for patients with specific diseases and across different clinical settings.